{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6d57547",
   "metadata": {},
   "outputs": [],
   "source": [
    "作业：\n",
    "\n",
    "使用逻辑回归算法对鸢尾花数据集（或其他数据集）建模进行分类预测\n",
    "\n",
    "按照序号1-6，完成要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92e6e1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris    #鸢尾花数据集\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression   # 逻辑回归\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e883e024",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.导入数据（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7309bf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris   # 鸢尾花数据集位于sklearn.datasets\n",
    "data = load_iris()   # 得到数据特征\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71e03e3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.keys())\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14f08708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data',\n",
       " 'target',\n",
       " 'frame',\n",
       " 'target_names',\n",
       " 'DESCR',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'data_module']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fecd27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target[[10, 50, 100]]   # 假设对样本 10、50 和 100 感兴趣，并且想知道它们的类名。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d52b8dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa', 'versicolor', 'virginica']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.target_names)     # 查看对应的类标签，其中0,1,2分别代表setosa，versicolor,virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f53101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target  # 得到数据对应的类标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00891bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names  # 得到数据对应的类标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c07e278",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "669b923e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_target=data.target  # 得到数据对应的类标签\n",
    "iris_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c7a5fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看每个类别的数量\n",
    "pd.Series(iris_target).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8dd18c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_features=pd.DataFrame(data=data.data,columns=data.feature_names) # 利用pandas转化为DataFrame格式\n",
    "iris_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c82d2c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "# 利用.info()查看数据的整体信息\n",
    "iris_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7577479d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于特征进行描述性统计\n",
    "iris_features.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638f148a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "747fc582",
   "metadata": {},
   "outputs": [],
   "source": [
    "X =load_iris().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e99fd35b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y =load_iris().target  # 得到数据对应的类标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fe7d435a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b63769e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y   # 得到数据对应的类标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "421ca965",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "41063cae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3\n",
       "0    5.1  3.5  1.4  0.2\n",
       "1    4.9  3.0  1.4  0.2\n",
       "2    4.7  3.2  1.3  0.2\n",
       "3    4.6  3.1  1.5  0.2\n",
       "4    5.0  3.6  1.4  0.2\n",
       "..   ...  ...  ...  ...\n",
       "145  6.7  3.0  5.2  2.3\n",
       "146  6.3  2.5  5.0  1.9\n",
       "147  6.5  3.0  5.2  2.0\n",
       "148  6.2  3.4  5.4  2.3\n",
       "149  5.9  3.0  5.1  1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X) #可以考虑去量纲(标准化)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d9b16161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1058.88x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 合并特征和类标签\n",
    "iris_all=iris_features.copy() # 进行浅拷贝，防止对于原始数据的修改\n",
    "iris_all['target']=iris_target\n",
    "## 特征与标签组合的散点可视化\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.pairplot(data=iris_all,diag_kind='hist',hue='target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d7cf622",
   "metadata": {},
   "outputs": [],
   "source": [
    "从上图可以发现，在2D情况下不同的特征组合对于不同类别的花的散点分布，以及大概的区分能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec10e204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22khSUlKSMjMzXbcjR46U+PMAAADew9DDUjk5OQUuoezr6yuHw1HkMnFxcfr444/dxtasWaO4uLhC59tsNtlstqsPCwAAygVD99x07dpVU6ZM0UcffaRDhw5pxYoVeuGFF3Tfffe55iQlJSkhIcF1f9CgQTpw4IBGjx6tXbt26ZVXXtGSJUs0YsQII54CAADwMobuuXnppZc0duxYDR48WCdPnlRERIQGDhyocePGueYcO3ZM6enprvtRUVH66KOPNGLECM2ePVs1atTQggULuMYNAACQZPB1bozAdW4AACh/ys11bgAAAEpahfviTABAxeB0OmW32w3PkJeXJ0ny8/Mr1pc+liar1Wp4hrJAuQEAmJLdbteYMWOMjuFVUlJSKsQniDksBQAATIU9NwAAU7JarUpJSTE0g91u19ixYyVJkydPltVqNTSP0dsvK5QbAIApWSwWrzoEY7VavSqPmXFYCgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmIqh5aZ27dqyWCwFbomJiYXOT01NLTDX39+/jFMDAABvVsnIjW/dulX5+fmu+zt37lSnTp3Uo0ePIpcJDAzU7t27XfctFkupZgQAAOWLoeUmNDTU7f7UqVNVp04dtW/fvshlLBaLwsPDSzsaAAAop7zmnBu73a7Fixfrscceu+TemOzsbNWqVUuRkZHq1q2bfvzxx0uuNzc3V1lZWW43AABgXl5TblauXKmzZ8+qb9++Rc6pX7++3njjDX3wwQdavHixHA6H2rRpo59//rnIZZKTkxUUFOS6RUZGlkJ6AADgLSxOp9NpdAhJio+Pl9Vq1apVq4q9TF5enho2bKhevXpp8uTJhc7Jzc1Vbm6u635WVpYiIyOVmZmpwMDAq84NAEBRcnNzNWbMGElSSkqKbDabwYnKr6ysLAUFBRXr97eh59z84fDhw1q7dq2WL1/u0XJ+fn6KjY3Vvn37ipxjs9l4MwEAUIF4xWGphQsXqmrVqrr77rs9Wi4/P187duxQtWrVSikZAAAobwwvNw6HQwsXLlSfPn1UqZL7jqSEhAQlJSW57k+aNEmrV6/WgQMHtG3bNj3yyCM6fPiwBgwYUNaxAQCAlzL8sNTatWuVnp6uxx57rMBj6enp8vH5X//KyMjQ448/ruPHjys4OFjNmzdXWlqaoqOjyzIyAADwYoaXm86dO6uoc5rXr1/vdn/mzJmaOXNmGaQCAADlleGHpQAAAEoS5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJgK5QYAAJiKoeWmdu3aslgsBW6JiYlFLrN06VI1aNBA/v7+aty4sT7++OMyTAwAALydoeVm69atOnbsmOu2Zs0aSVKPHj0KnZ+WlqZevXqpf//++v7779W9e3d1795dO3fuLMvYAADAi1UycuOhoaFu96dOnao6deqoffv2hc6fPXu27rzzTj399NOSpMmTJ2vNmjWaM2eO5s2bV+p5ARTO6XTKbrcbniEvL0+S5OfnJ4vFYmgeq9VqeAagojK03FzMbrdr8eLFGjlyZJH/IGzevFkjR450G4uPj9fKlSuLXG9ubq5yc3Nd97OyskokL4D/sdvtGjNmjNExvEpKSopsNpvRMYAKyWtOKF65cqXOnj2rvn37Fjnn+PHjCgsLcxsLCwvT8ePHi1wmOTlZQUFBrltkZGRJRQYAAF7Ia/bcvP766+rSpYsiIiJKdL1JSUlue3uysrIoOEAJs1qtSklJMTSD3W7X2LFjJV04ZG21Wg3NY/T2gYrMK8rN4cOHtXbtWi1fvvyS88LDw3XixAm3sRMnTig8PLzIZWw2G7uGgVJmsVi86u+Z1Wr1qjwAypZXHJZauHChqlatqrvvvvuS8+Li4rRu3Tq3sTVr1iguLq404wEAgHLE8HLjcDi0cOFC9enTR5Uque9ISkhIUFJSkuv+sGHD9Omnn2rGjBnatWuXJkyYoG+//VZDhgwp69gAAMBLGV5u1q5dq/T0dD322GMFHktPT9exY8dc99u0aaO3335b8+fPV5MmTbRs2TKtXLlSMTExZRkZAAB4McPPuencubOcTmehj61fv77AWI8ePYq8yB8AAIDh5QYAYC7ecFFHb3Hx68BrckFZXOCScgMAKFFc1LFwf1yqoKIriwtcGn7ODQAAQElizw0AoNTc1K+5fPwq7v+jnU6nnL87JEmWSj4V9vvGHHkO7V34XZltj3IDACg1Pn4+8vHzNTqGsbhYdZmruHUaAACYEuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYCuUGAACYikffCn727FmtWLFCX375pQ4fPqycnByFhoYqNjZW8fHxatOmTWnlBAAAKJZi7bn55ZdfNGDAAFWrVk3PPvuszp8/r6ZNm+qOO+5QjRo19MUXX6hTp06Kjo7We++9V9qZAQAAilSsPTexsbHq06ePvvvuO0VHRxc65/z581q5cqVmzZqlI0eOaNSoUSUaFAAAoDiKVW7+85//KCQk5JJzAgIC1KtXL/Xq1UtnzpwpkXAAAACeKtZhqcsVm6udDwAAUFI8OqH4D7/88os2bdqkkydPyuFwuD02dOjQEgkGAABwJTwuN6mpqRo4cKCsVqtCQkJksVhcj1ksFsoNAAAwlMflZuzYsRo3bpySkpLk48NlcgAAgHfxuJ3k5OTooYceotgAAACv5HFD6d+/v5YuXVoaWQAAAK6ax4elkpOTdc899+jTTz9V48aN5efn5/b4Cy+8UGLhAAAAPHVF5eazzz5T/fr1JanACcUAAABG8rjczJgxQ2+88Yb69u1bCnEAAACujsfn3NhsNrVt27Y0sgAAAFw1j8vNsGHD9NJLL5VGFgAAgKvm8WGpb775Rp9//rn+9a9/qVGjRgVOKF6+fHmJhQMAAPCUx+WmSpUquv/++0sjCwAAwFXzuNwsXLiwRAMcPXpUY8aM0SeffKKcnBzVrVtXCxcuVIsWLQqdv379et1+++0Fxo8dO6bw8PASzQYAAMofj8vNwYMH9fvvv+umm25yG9+7d6/8/PxUu3btYq8rIyNDbdu21e23365PPvlEoaGh2rt3r4KDgy+77O7duxUYGOi6X7Vq1WJvFwAAmJfH5aZv37567LHHCpSbr7/+WgsWLND69euLva6UlBRFRka67Q2Kiooq1rJVq1ZVlSpVir0tAABQMXhcbr7//vtCPwp+yy23aMiQIR6t68MPP1R8fLx69OihDRs2qHr16ho8eLAef/zxyy7btGlT5ebmKiYmRhMmTODj6QDgJZxOp+tnR16+gUngLS5+H1z8/igtHpcbi8Wic+fOFRjPzMxUfr5nb+IDBw5o7ty5GjlypP7+979r69atGjp0qKxWq/r06VPoMtWqVdO8efPUokUL5ebmasGCBerQoYO+/vprNWvWrMD83Nxc5ebmuu5nZWV5lBEA4Jm8vDzXz3sXbjMwCbxRXl6e/P39S3UbHpeb2267TcnJyXrnnXfk6+srScrPz1dycrJuvfVWj9blcDjUokULPffcc5Kk2NhY7dy5U/PmzSuy3NSvX9/11Q+S1KZNG+3fv18zZ87UP//5zwLzk5OTNXHiRI9yAQCA8svjcpOSkqLbbrtN9evXV7t27SRJX375pbKysvT55597tK5q1aopOjrabaxhw4Z6//33PVpPq1attGnTpkIfS0pK0siRI133s7KyFBkZ6dH6AQDFd/H1z27q10w+fr4GpoE3cOTlu/bi/fn6eKXB43ITHR2tf//735ozZ462b9+ugIAAJSQkaMiQIbr++us9Wlfbtm21e/dut7E9e/aoVq1aHq3nhx9+ULVq1Qp9zGazyWazebQ+AMCVu/hLlH38fCk3cFMWX7LtcbmRpIiICNehpKsxYsQItWnTRs8995x69uypb775RvPnz9f8+fNdc5KSknT06FEtWrRIkjRr1ixFRUWpUaNG+u9//6sFCxbo888/1+rVq686DwAAKP+K9d1S6enpHq306NGjxZrXsmVLrVixQu+8845iYmI0efJkzZo1S71793bNOXbsmNv27Xa7/va3v6lx48Zq3769tm/frrVr1+qOO+7wKCMAADCnYu25admypbp3764BAwaoZcuWhc7JzMzUkiVLNHv2bD3xxBMaOnRosQLcc889uueee4p8PDU11e3+6NGjNXr06GKtGwAAVDzFKjf/+c9/NGXKFHXq1En+/v5q3ry5IiIi5O/vr4yMDP3nP//Rjz/+qGbNmun555/XXXfdVdq5AQAAClWsw1IhISF64YUXdOzYMc2ZM0c33XSTTp8+rb1790qSevfure+++06bN2+m2AAAAEN5dEJxQECAHnjgAT3wwAOllQcAAOCqFGvPDQAAQHlBuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZyRV+/sHfvXn3xxRc6efKkHA6H22Pjxo0rkWAAAABXwuNy89prr+nJJ5/UDTfcoPDwcLcvwLJYLJQbAABgKI/LzbPPPqspU6ZozJgxpZEHgIecTqfsdrvRMQx38WvA63GB1Wotk29gBryNx+UmIyNDPXr0KI0sAK6A3W7nPxt/MnbsWKMjeIWUlBTZbDajYwBlzuMTinv06KHVq1eXRhYAAICrVqw9Ny+++KLr57p162rs2LHasmWLGjduLD8/P7e5xf02cAAlb9TttWT1rZgfgnQ6ncpzOCVJfj6WCns4xp7v0PQvDhsdAzBUscrNzJkz3e5fd9112rBhgzZs2OA2brFYKDeAgay+PrJWqpjlRpI4AANAKma5OXjwYGnnAAAAKBEe/xdv0qRJysnJKTB+/vx5TZo0qURCAQAAXCmPPy01ceJEDRo0SNdcc43beE5OjiZOnMh1bgAALo48x+UnmZjT6ZTz9wuvgaWST4U9F6ys3wcelxun01noH8727dt1/fXXl0goAIA57F34ndERUAEVu9wEBwfLYrnwCYR69eq5FZz8/HxlZ2dr0KBBpRISAACguIpdbmbNmiWn06nHHntMEydOVFBQkOsxq9Wq2rVrKy4urlRCAgDKD6vVqpSUFKNjeAW73e66qOTkyZNltVoNTmS8sngNil1u+vTpI0mKiopSmzZtClzfBgAA6cJlQbgyckFWq5XXpYx4fM5NbGyszp8/r/Pnz7uN//FmppUCAAAjeVxuqlSpcsmzvWvUqKG+fftq/Pjx8vGpuBcTAwAAxvC43KSmpuof//iH+vbtq1atWkmSvvnmG7355pv6v//7P506dUrTp0+XzWbT3//+9xIPDAAAcCkel5s333xTM2bMUM+ePV1jXbt2VePGjfXqq69q3bp1qlmzpqZMmUK5AQAAZc7j40ZpaWmKjY0tMB4bG6vNmzdLkm699Valp6dffToAAAAPeVxuIiMj9frrrxcYf/311xUZGSlJOnPmjIKDg68+HQAAgIc8Piw1ffp09ejRQ5988olatmwpSfr222+1a9cuLVu2TJK0detWPfjggyWbFAAAoBg8Ljf33nuvdu3apVdffVV79uyRJHXp0kUrV65U7dq1JUlPPvlkiYYEAAAoLo/LjXThQn5Tp04t6SwAAABX7YrKzdmzZ/XNN9/o5MmTcjjcv+kzISGhRIIBAABcCY/LzapVq9S7d29lZ2crMDDQ7YJ+FouFcgMAAAzl8ael/va3v+mxxx5Tdna2zp49q4yMDNft119/LY2MAAAAxeZxuTl69KiGDh2qa665pjTyAAAAXBWPy018fLy+/fbb0sgCAABw1TwuN3fffbeefvppTZgwQe+//74+/PBDt5unjh49qkceeUQhISEKCAhQ48aNL1ue1q9fr2bNmslms6lu3bpKTU31eLsAAMCcPD6h+PHHH5ckTZo0qcBjFotF+fn5xV5XRkaG2rZtq9tvv12ffPKJQkNDtXfv3kte3fjgwYO6++67NWjQIL311ltat26dBgwYoGrVqik+Pt7TpwMAAEzG43Lz549+X42UlBRFRkZq4cKFrrGoqKhLLjNv3jxFRUVpxowZkqSGDRtq06ZNmjlzJuUGAAB4fljqYv/973+vauMffvihWrRooR49eqhq1aqKjY3Va6+9dsllNm/erI4dO7qNxcfHu760889yc3OVlZXldgMAAOblcbnJz8/X5MmTVb16dV133XU6cOCAJGns2LGFfqHmpRw4cEBz587VTTfdpM8++0xPPvmkhg4dqjfffLPIZY4fP66wsDC3sbCwMGVlZen8+fMF5icnJysoKMh1++PLPQEAgDl5XG6mTJmi1NRUPf/887Jara7xmJgYLViwwKN1ORwONWvWTM8995xiY2P1xBNP6PHHH9e8efM8jVWkpKQkZWZmum5HjhwpsXUDAADv43G5WbRokebPn6/evXvL19fXNd6kSRPt2rXLo3VVq1ZN0dHRbmMNGzZUenp6kcuEh4frxIkTbmMnTpxQYGCgAgICCsy32WwKDAx0uwEAAPO6oov41a1bt8C4w+FQXl6eR+tq27atdu/e7Ta2Z88e1apVq8hl4uLitG7dOrexNWvWKC4uzqNtAwAAc/K43ERHR+vLL78sML5s2TLFxsZ6tK4RI0Zoy5Yteu6557Rv3z69/fbbmj9/vhITE11zkpKS3L6vatCgQTpw4IBGjx6tXbt26ZVXXtGSJUs0YsQIT58KAAAwIY8/Cj5u3Dj16dNHR48elcPh0PLly7V7924tWrRI//rXvzxaV8uWLbVixQolJSVp0qRJioqK0qxZs9S7d2/XnGPHjrkdpoqKitJHH32kESNGaPbs2apRo4YWLFjAx8ABAICkKyg33bp106pVqzRp0iRde+21GjdunJo1a6ZVq1apU6dOHge45557dM899xT5eGFXH+7QoYO+//57j7cFAADMz+NyI0nt2rXTmjVrSjoLAADAVbuicgPAezidTtfP9vySu4I4yqeL3wMXvzeAiqRY5SY4OFgWi6VYK/z111+vKhAAz1z8KcXpXxw2MAm8TV5envz9/Y2OAZS5YpWbWbNmlXIMAACAklGsctOnT5/SzgHgCvn5+bl+HnV7LVl9r+or41DO2fMdrj14F783gIqEc26Acu7iQ8ZWXx9ZK1FucEFxTycAzIZ/BQEAgKlQbgAAgKlQbgAAgKlQbgAAgKkU64Ti+++/v9grXL58+RWHQfE4nU7Z7XbDM/xxfRU/Pz/DT1y0Wq2GZwAAeIdilZugoKDSzgEP2O12jRkzxugYXiUlJUU2m83oGAAAL1CscrNw4cLSzgEAAFAiuM5NOWS1WpWSkmJoBrvdrrFjx0qSJk+eLKvVamgeo7cPAPAeV1Ruli1bpiVLlig9Pb3AuR/btm0rkWAomsVi8apDMFar1avyAAAqNo8/LfXiiy+qX79+CgsL0/fff69WrVopJCREBw4cUJcuXUojIwAAQLF5XG5eeeUVzZ8/Xy+99JKsVqtGjx6tNWvWaOjQocrMzCyNjAAAAMXmcblJT09XmzZtJEkBAQE6d+6cJOnRRx/VO++8U7LpAAAAPORxuQkPD9evv/4qSapZs6a2bNkiSTp48KCcTmfJpgMAAPCQx+XmL3/5iz788ENJUr9+/TRixAh16tRJDz74oO67774SDwgAAOAJjz8tNX/+fDkcDklSYmKiQkJClJaWpnvvvVcDBw4s8YAAAACe8Ljc+Pj4yMfnfzt8HnroIT300EMlGgoAAOBKXdF1bjIyMvT666/rp59+kiRFR0erX79+uv7660s0HAAAgKc8Pudm48aNioqK0osvvqiMjAxlZGToxRdfVFRUlDZu3FgaGQEAAIrN4z03iYmJ6tmzp+bOnStfX19JUn5+vgYPHqzExETt2LGjxEMCAAAUl8d7bvbt26e//e1vrmIjSb6+vho5cqT27dtXouEAAAA85XG5adasmetcm4v99NNPatKkSYmEAgAAuFIeH5YaOnSohg0bpn379umWW26RJG3ZskUvv/yypk6dqn//+9+uuTfffHPJJQUAACgGj8tNr169JEmjR48u9DGLxSKn0ymLxaL8/PyrTwgAAOABj8vNwYMHSyMHgBJgz3cYHcEwTqdTeY4LXwHj52ORxWIxOJExKvJ74M+cTqfsdruhGS7evtFZJMlqtVaIvxsel5tatWqVRg4AJWD6F4eNjgB4DbvdrjFjxhgdw2Xs2LFGR1BKSopsNpvRMUqdxycUS9I///lPtW3bVhERETp8+MI/prNmzdIHH3xQouEAAAA85fGem7lz52rcuHEaPny4pkyZ4jqvpkqVKpo1a5a6detW4iEBFM1qtSolJcXoGIaz2+2u/xlPnjxZVqvV4ETGq+ivgTf83XA6ncrLy5Mk+fn5GX5IqKK8JzwuNy+99JJee+01de/eXVOnTnWNt2jRQqNGjSrRcAAuz2KxVIjdzJ6wWq28JvCavxv+/v5GR6hwPD4sdfDgQcXGxhYYt9ls+u2330okFAAAwJXyuNxERUXphx9+KDD+6aefqmHDhiWRCQAA4Ip5XG5GjhypxMREvffee3I6nfrmm280ZcoUJSUlFXrtm0uZMGGCLBaL261BgwZFzk9NTS0wn919AADgYh6fczNgwAAFBATo//7v/5STk6OHH35YERERmj17th566CGPAzRq1Ehr1679X6BKl44UGBio3bt3u+4bfXIWAADwLh6XG0nq3bu3evfurZycHGVnZ6tq1apXHqBSJYWHhxd7vsVi8Wg+AACoWDw+LHX+/Hnl5ORIkq655hqdP39es2bN0urVq68owN69exUREaEbb7xRvXv3Vnp6+iXnZ2dnq1atWoqMjFS3bt30448/XnJ+bm6usrKy3G4AAMC8PC433bp106JFiyRJZ8+eVatWrTRjxgx169ZNc+fO9WhdrVu3Vmpqqj799FPNnTtXBw8eVLt27XTu3LlC59evX19vvPGGPvjgAy1evFgOh0Nt2rTRzz//XOQ2kpOTFRQU5LpFRkZ6lBEAAJQvHpebbdu2qV27dpKkZcuWKTw8XIcPH9aiRYv04osverSuLl26qEePHrr55psVHx+vjz/+WGfPntWSJUsKnR8XF6eEhAQ1bdpU7du31/LlyxUaGqpXX321yG0kJSUpMzPTdTty5IhHGQEAQPni8Tk3OTk5qly5siRp9erVuv/+++Xj46NbbrnF9VUMV6pKlSqqV6+e9u3bV6z5fn5+io2NveR8m83mFRdxAgAAZcPjPTd169bVypUrdeTIEX322Wfq3LmzJOnkyZMKDAy8qjDZ2dnav3+/qlWrVqz5+fn52rFjR7HnAwAA8/O43IwbN06jRo1S7dq11bp1a8XFxUm6sBensCsXX8qoUaO0YcMGHTp0SGlpabrvvvvk6+urXr16SZISEhKUlJTkmj9p0iStXr1aBw4c0LZt2/TII4/o8OHDGjBggKdPAwAAmJTHh6UeeOAB3XrrrTp27JiaNGniGr/jjjt03333ebSun3/+Wb169dKZM2cUGhqqW2+9VVu2bFFoaKgkKT09XT4+/+tfGRkZevzxx3X8+HEFBwerefPmSktLU3R0tKdPAwAAmNQVXecmPDy8wLVmWrVq5fF63n333Us+vn79erf7M2fO1MyZMz3eDgAAqDg8PiwFAADgzSg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCg3AADAVCoZHaC8cTqdstvtRscw3MWvAa/HBVarVRaLxegYAFDhUW48ZLfbNWbMGKNjeJWxY8caHcErpKSkyGazGR0DACo8DksBAABTYc/NVWh1/3D5VvIzOoYhnE6nHPl5kiQfX78Kezgm//c8fbN8ltExAAAXodxcBd9KfvKtZDU6hnH8OAQDAPA+HJYCAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmYmi5mTBhgiwWi9utQYMGl1xm6dKlatCggfz9/dW4cWN9/PHHZZQWAACUB4bvuWnUqJGOHTvmum3atKnIuWlpaerVq5f69++v77//Xt27d1f37t21c+fOMkwMAAC8meHlplKlSgoPD3fdbrjhhiLnzp49W3feeaeefvppNWzYUJMnT1azZs00Z86cMkwMAAC8WSWjA+zdu1cRERHy9/dXXFyckpOTVbNmzULnbt68WSNHjnQbi4+P18qVK8sg6QVOp9P1c/7v9jLbLrzTxe+Bi98bAADjGFpuWrdurdTUVNWvX1/Hjh3TxIkT1a5dO+3cuVOVK1cuMP/48eMKCwtzGwsLC9Px48eL3EZubq5yc3Nd97Oysq4qc15enuvnb5bPvqp1wVzy8vLk7+9vdAwAqPAMLTddunRx/XzzzTerdevWqlWrlpYsWaL+/fuXyDaSk5M1ceLEElkXAADwfoYflrpYlSpVVK9ePe3bt6/Qx8PDw3XixAm3sRMnTig8PLzIdSYlJbkdysrKylJkZOQVZ/Tz83P93Or+YfKtZL3idaH8y//d7tqDd/F7AwBgHK8qN9nZ2dq/f78effTRQh+Pi4vTunXrNHz4cNfYmjVrFBcXV+Q6bTabbDZbiWW0WCyun30rWSk3cLn4vQEAMI6hn5YaNWqUNmzYoEOHDiktLU333XeffH191atXL0lSQkKCkpKSXPOHDRumTz/9VDNmzNCuXbs0YcIEffvttxoyZIhRTwEAAHgZQ8vNzz//rF69eql+/frq2bOnQkJCtGXLFoWGhkqS0tPTdezYMdf8Nm3a6O2339b8+fPVpEkTLVu2TCtXrlRMTIxRTwEAgEvauXOnJk6cyDXZypChh6XefffdSz6+fv36AmM9evRQjx49SikRAAAlx263a+nSpcrMzNTSpUtVr149Wa2czlDaDL+IHwAAZrV27VrXJUiysrK0du1agxNVDF51QjGA8snpdMpuN/ailhdv3+gskmS1WjnJvII7deqU1q5d67rAp9Pp1Lp169SyZUvX6RcoHZQbAFfNbrdrzJgxRsdwGTt2rNERlJKSUqKf1ET54nQ69f777xc5PnDgQMpvKeKwFAAAJezEiRPatWuXHA6H27jD4dCuXbsKXLMNJYs9NwCumtVqVUpKiqEZnE6n6+tR/Pz8DP9fMSeNVmxhYWFq0KCB9uzZ41ZwfHx8VK9evQJfJYSSRbkBcNUsFotXHILhu73gLSwWi/76178qOTm5wPgDDzxgePk2Ow5LAQBQCkJDQ9WxY0dXkbFYLLrjjjt0ww03GJzM/Cg3AACUko4dOyowMFCSFBQUpI4dOxqcqGKg3AAAUEqsVqt69Oih4OBgPfDAA5yLVUY45wYAgFIUExPD1wSVMcrNVcj/Pc/oCIZxOp1y5F94/j6+xn8yxSgV+T0AAN6KcnMVvlk+y+gIAADgTzjnBgAAmAp7bjzkDRcr8wZ2u911ifvJkydzkpy4aBsAeAvKjYe85WJl3sRqtfKaAAC8BoelAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqXhNuZk6daosFouGDx9e5JzU1FRZLBa3m7+/f9mFBAAAXq+S0QEkaevWrXr11Vd18803X3ZuYGCgdu/e7bpvsVhKMxoAAChnDN9zk52drd69e+u1115TcHDwZedbLBaFh4e7bmFhYWWQEgAAlBeGl5vExETdfffd6tixY7HmZ2dnq1atWoqMjFS3bt30448/XnJ+bm6usrKy3G4AAMC8DC037777rrZt26bk5ORiza9fv77eeOMNffDBB1q8eLEcDofatGmjn3/+uchlkpOTFRQU5LpFRkaWVHwAAOCFDCs3R44c0bBhw/TWW28V+6TguLg4JSQkqGnTpmrfvr2WL1+u0NBQvfrqq0Uuk5SUpMzMTNftyJEjJfUUAACAFzLshOLvvvtOJ0+eVLNmzVxj+fn52rhxo+bMmaPc3Fz5+vpech1+fn6KjY3Vvn37ipxjs9lks9lKLDcAAPBuhpWbO+64Qzt27HAb69evnxo0aKAxY8ZctthIF8rQjh07dNddd5VWTAAAUM4YVm4qV66smJgYt7Frr71WISEhrvGEhARVr17ddU7OpEmTdMstt6hu3bo6e/aspk2bpsOHD2vAgAFlnh8AAHgnr7jOTVHS09Pl4/O/04IyMjL0+OOP6/jx4woODlbz5s2Vlpam6OhoA1MCAABv4lXlZv369Ze8P3PmTM2cObPsAgEAgHLH8OvcAAAAlCTKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBWv+voFFI/T6ZTdbjc0w8XbNzqLJFmtVlksFqNjAAC8gMXpdDqNDlGWsrKyFBQUpMzMTAUGBhod54rk5uZqzJgxRsfwKikpKbLZbEbHAACUEk9+f3NYCgAAmAqHpcohq9WqlJQUQzM4nU7l5eVJkvz8/Aw/JGS1Wg3dPgDAe1BuyiGLxeIVh2D8/f2NjgAAQAEclgIAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZS4b4V3Ol0SpKysrIMTgIAAIrrj9/bf/wev5QKV27OnTsnSYqMjDQ4CQAA8NS5c+cUFBR0yTkWZ3EqkIk4HA798ssvqly5siwWi9FxyrWsrCxFRkbqyJEjCgwMNDoOwHsSXon3ZclwOp06d+6cIiIi5ONz6bNqKtyeGx8fH9WoUcPoGKYSGBjIX1h4Fd6T8Ea8L6/e5fbY/IETigEAgKlQbgAAgKlQbnDFbDabxo8fL5vNZnQUQBLvSXgn3pdlr8KdUAwAAMyNPTcAAMBUKDcAAMBUKDcAAMBUKDcAAMBUKDe4Ii+//LJq164tf39/tW7dWt98843RkVCBbdy4UV27dlVERIQsFotWrlxpdCRUcMnJyWrZsqUqV66sqlWrqnv37tq9e7fRsSoMyg089t5772nkyJEaP368tm3bpiZNmig+Pl4nT540OhoqqN9++01NmjTRyy+/bHQUQJK0YcMGJSYmasuWLVqzZo3y8vLUuXNn/fbbb0ZHqxD4KDg81rp1a7Vs2VJz5syRdOH7uiIjI/XUU0/pmWeeMTgdKjqLxaIVK1aoe/fuRkcBXE6dOqWqVatqw4YNuu2224yOY3rsuYFH7Ha7vvvuO3Xs2NE15uPjo44dO2rz5s0GJgMA75WZmSlJuv766w1OUjFQbuCR06dPKz8/X2FhYW7jYWFhOn78uEGpAMB7ORwODR8+XG3btlVMTIzRcSqECvet4AAAlKXExETt3LlTmzZtMjpKhUG5gUduuOEG+fr66sSJE27jJ06cUHh4uEGpAMA7DRkyRP/617+0ceNG1ahRw+g4FQaHpeARq9Wq5s2ba926da4xh8OhdevWKS4uzsBkAOA9nE6nhgwZohUrVujzzz9XVFSU0ZEqFPbcwGMjR45Unz591KJFC7Vq1UqzZs3Sb7/9pn79+hkdDRVUdna29u3b57p/8OBB/fDDD7r++utVs2ZNA5OhokpMTNTbb7+tDz74QJUrV3adkxgUFKSAgACD05kfHwXHFZkzZ46mTZum48ePq2nTpnrxxRfVunVro2Ohglq/fr1uv/32AuN9+vRRampq2QdChWexWAodX7hwofr27Vu2YSogyg0AADAVzrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBAACmQrkBYKgOHTpo+PDhRsdw8bY8ADxHuQFQ7tntdqMjAPAilBsAhunbt682bNig2bNny2KxyGKxaP/+/erfv7+ioqIUEBCg+vXra/bs2QWW6969u6ZMmaKIiAjVr19fkpSWlqamTZvK399fLVq00MqVK2WxWPTDDz+4lt25c6e6dOmi6667TmFhYXr00Ud1+vTpIvMcOnSorF4OACWEbwUHYJjZs2drz549iomJ0aRJkyRJwcHBqlGjhpYuXaqQkBClpaXpiSeeULVq1dSzZ0/XsuvWrVNgYKDWrFkjScrKylLXrl1111136e2339bhw4cLHF46e/as/vKXv2jAgAGaOXOmzp8/rzFjxqhnz576/PPPC80TGhpaNi8GgBJDuQFgmKCgIFmtVl1zzTUKDw93jU+cONH1c1RUlDZv3qwlS5a4lZtrr71WCxYskNVqlSTNmzdPFotFr732mvz9/RUdHa2jR4/q8ccfdy0zZ84cxcbG6rnnnnONvfHGG4qMjNSePXtUr169QvMAKF8oNwC8zssvv6w33nhD6enpOn/+vOx2u5o2beo2p3Hjxq5iI0m7d+/WzTffLH9/f9dYq1at3JbZvn27vvjiC1133XUFtrl//37Vq1evZJ8IAENQbgB4lXfffVejRo3SjBkzFBcXp8qVK2vatGn6+uuv3eZde+21Hq87OztbXbt2VUpKSoHHqlWrdsWZAXgXyg0AQ1mtVuXn57vuf/XVV2rTpo0GDx7sGtu/f/9l11O/fn0tXrxYubm5stlskqStW7e6zWnWrJnef/991a5dW5UqFf7P35/zACh/+LQUAEPVrl1bX3/9tQ4dOqTTp0/rpptu0rfffqvPPvtMe/bs0dixYwuUlMI8/PDDcjgceuKJJ/TTTz/ps88+0/Tp0yVJFotFkpSYmKhff/1VvXr10tatW7V//3599tln6tevn6vQ/DmPw+EovScPoFRQbgAYatSoUfL19VV0dLRCQ0MVHx+v+++/Xw8++KBat26tM2fOuO3FKUpgYKBWrVqlH374QU2bNtU//vEPjRs3TpJc5+FEREToq6++Un5+vjp37qzGjRtr+PDhqlKlinx8fArNk56eXnpPHkCpsDidTqfRIQCgNLz11lvq16+fMjMzFRAQYHQcAGWEc24AmMaiRYt04403qnr16tq+fbvrGjYUG6BiodwAMI3jx49r3LhxOn78uKpVq6YePXpoypQpRscCUMY4LAUAAEyFE4oBAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICp/D8DnykB0pp2GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用箱型图也可以得到不同特征上的分布差异情况。\n",
    "for col in iris_features:\n",
    "    sns.boxplot(x='target',y=col,palette='pastel',data=iris_all)\n",
    "    plt.title(col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da7055c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15aa0340",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cee21dcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR #逻辑回归\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3525a5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LR(\n",
    "#     penalty='l2', # l2正则化---岭回归   l1正则化---lasso  默认l2\n",
    "#     *,\n",
    "#     dual=False,\n",
    "#     tol=0.0001,\n",
    "#     C=1.0,    # C越小表示惩罚力度越大，C越大惩罚力度越小\n",
    "#     fit_intercept=True,\n",
    "#     intercept_scaling=1,\n",
    "#     class_weight=None,\n",
    "#     random_state=None,\n",
    "#     solver='lbfgs', # 梯度下降的方式\n",
    "#     max_iter=100, # 梯度下降会有迭代次数\n",
    "#     multi_class='auto',\n",
    "#     verbose=0,\n",
    "#     warm_start=False,\n",
    "#     n_jobs=None,\n",
    "#     l1_ratio=None,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "16724332",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr1 = LR(penalty='l1',solver='liblinear', #l1 正则化   #liblinear 坐标下降法\n",
    "                       C= 0.5,     ##逻辑回归的重要属性coef_，查看每个特征所对应的参数\n",
    "                       max_iter=1000).fit(X,Y)\n",
    "\n",
    "lr2 = LR(penalty='l2',solver='liblinear', #l2 正则化\n",
    "                       C= 0.5,\n",
    "                       max_iter=1000).fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d0b0eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96, 0.9666666666666667)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1.score(X,Y),lr2.score(X,Y)      # L1正则化可以对特征进行筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f76e22d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  2.09072083, -2.36318641,  0.        ],\n",
       "       [ 0.52713635, -1.32460753,  0.24065339, -0.64469775],\n",
       "       [-2.02487066, -1.51427237,  2.41526217,  3.15936762]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1.coef_ # w 参数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "87d96194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3487989 ,  1.24412961, -1.93156823, -0.86728358],\n",
       "       [ 0.34730017, -1.30134156,  0.45617726, -0.98309651],\n",
       "       [-1.33632956, -1.13117855,  1.91543771,  1.87050334]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2.coef_ # w 参数值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "57c78c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  2.09159   , -2.36426899,  0.        ],\n",
       "       [ 0.5274624 , -1.3249617 ,  0.24027731, -0.64421089],\n",
       "       [-2.02505332, -1.51413907,  2.41546765,  3.1592145 ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  lrl1 = lr1.fit(X,Y)\n",
    "#  lrl1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "467db11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 4])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (lrl1.coef_ != 0).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "79ff9caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3487989 ,  1.24412961, -1.93156823, -0.86728358],\n",
       "       [ 0.34730017, -1.30134156,  0.45617726, -0.98309651],\n",
       "       [-1.33632956, -1.13117855,  1.91543771,  1.87050334]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lrl2 = lr2.fit(X,Y)\n",
    "# lrl2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1197f259",
   "metadata": {},
   "outputs": [],
   "source": [
    "可以看见，当我们选择L1正则化的时候，许多特征的参数都被设置为了0，这些特征在真正建模的时\n",
    "候，就不会出现在我们的模型当中了，而L2正则化则是对所有的特征都给出了参数。\n",
    "究竟哪个正则化的效果更好呢？还是都差不多？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d9f2f370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.68490137e-01, 1.31333829e-01, 1.76033668e-04],\n",
       "       [7.99953552e-01, 1.99662550e-01, 3.83898191e-04],\n",
       "       [8.45892443e-01, 1.53764593e-01, 3.42964368e-04],\n",
       "       [8.20666523e-01, 1.78690648e-01, 6.42828196e-04],\n",
       "       [8.84147647e-01, 1.15669900e-01, 1.82453845e-04],\n",
       "       [9.08949245e-01, 9.08471084e-02, 2.03646597e-04],\n",
       "       [8.80096134e-01, 1.19427640e-01, 4.76225247e-04],\n",
       "       [8.52658365e-01, 1.47070400e-01, 2.71234847e-04],\n",
       "       [8.01723964e-01, 1.97419174e-01, 8.56862187e-04],\n",
       "       [7.98463459e-01, 2.01193139e-01, 3.43402649e-04],\n",
       "       [8.79476897e-01, 1.20408291e-01, 1.14812302e-04],\n",
       "       [8.53447313e-01, 1.46117812e-01, 4.34875717e-04],\n",
       "       [7.92630568e-01, 2.07009879e-01, 3.59552617e-04],\n",
       "       [8.32163542e-01, 1.67429581e-01, 4.06876440e-04],\n",
       "       [9.12908322e-01, 8.70641528e-02, 2.75248002e-05],\n",
       "       [9.48663204e-01, 5.12829411e-02, 5.38552429e-05],\n",
       "       [9.23216048e-01, 7.66895882e-02, 9.43639897e-05],\n",
       "       [8.77507602e-01, 1.22277382e-01, 2.15015987e-04],\n",
       "       [8.81744180e-01, 1.18133210e-01, 1.22610491e-04],\n",
       "       [9.07103376e-01, 9.27059156e-02, 1.90708673e-04],\n",
       "       [8.25807650e-01, 1.73963917e-01, 2.28433173e-04],\n",
       "       [9.04056515e-01, 9.56852520e-02, 2.58232565e-04],\n",
       "       [9.11405040e-01, 8.84476775e-02, 1.47282831e-04],\n",
       "       [8.50766507e-01, 1.48521137e-01, 7.12355545e-04],\n",
       "       [8.32773698e-01, 1.66440919e-01, 7.85383264e-04],\n",
       "       [7.80183348e-01, 2.19324523e-01, 4.92129731e-04],\n",
       "       [8.65640852e-01, 1.33865467e-01, 4.93680751e-04],\n",
       "       [8.59897590e-01, 1.39916785e-01, 1.85625616e-04],\n",
       "       [8.51469627e-01, 1.48360820e-01, 1.69552173e-04],\n",
       "       [8.26133033e-01, 1.73253154e-01, 6.13812490e-04],\n",
       "       [8.05634907e-01, 1.93774800e-01, 5.90292857e-04],\n",
       "       [8.58482840e-01, 1.41284204e-01, 2.32956502e-04],\n",
       "       [9.19547413e-01, 8.03707018e-02, 8.18853560e-05],\n",
       "       [9.30685034e-01, 6.92658349e-02, 4.91306781e-05],\n",
       "       [8.09517012e-01, 1.90061093e-01, 4.21895573e-04],\n",
       "       [8.41068019e-01, 1.58745351e-01, 1.86630207e-04],\n",
       "       [8.60305788e-01, 1.39610552e-01, 8.36598151e-05],\n",
       "       [8.78530848e-01, 1.21297523e-01, 1.71629005e-04],\n",
       "       [8.25949955e-01, 1.73412498e-01, 6.37546709e-04],\n",
       "       [8.49209104e-01, 1.50554880e-01, 2.36016041e-04],\n",
       "       [8.85305248e-01, 1.14491000e-01, 2.03751876e-04],\n",
       "       [7.02779217e-01, 2.95850115e-01, 1.37066831e-03],\n",
       "       [8.56148220e-01, 1.43331247e-01, 5.20532570e-04],\n",
       "       [8.94981852e-01, 1.04353655e-01, 6.64492460e-04],\n",
       "       [8.96970721e-01, 1.02522121e-01, 5.07158129e-04],\n",
       "       [8.14700825e-01, 1.84756447e-01, 5.42727915e-04],\n",
       "       [8.95416251e-01, 1.04394072e-01, 1.89677499e-04],\n",
       "       [8.43247374e-01, 1.56274505e-01, 4.78121444e-04],\n",
       "       [8.82526572e-01, 1.17341629e-01, 1.31799287e-04],\n",
       "       [8.44039758e-01, 1.55712686e-01, 2.47555800e-04],\n",
       "       [4.78372357e-02, 7.59616677e-01, 1.92546088e-01],\n",
       "       [5.40819704e-02, 6.09325165e-01, 3.36592864e-01],\n",
       "       [2.12371621e-02, 6.60292976e-01, 3.18469862e-01],\n",
       "       [2.40230515e-02, 5.87661341e-01, 3.88315608e-01],\n",
       "       [2.01157323e-02, 6.24404019e-01, 3.55480248e-01],\n",
       "       [1.98194929e-02, 5.25618264e-01, 4.54562243e-01],\n",
       "       [3.34935806e-02, 4.86238754e-01, 4.80267665e-01],\n",
       "       [1.39366600e-01, 6.55668133e-01, 2.04965267e-01],\n",
       "       [3.08603425e-02, 7.29022207e-01, 2.40117451e-01],\n",
       "       [4.95352317e-02, 4.76082297e-01, 4.74382471e-01],\n",
       "       [4.71523984e-02, 6.79197838e-01, 2.73649763e-01],\n",
       "       [5.79232468e-02, 5.41682486e-01, 4.00394267e-01],\n",
       "       [3.41982340e-02, 7.92709897e-01, 1.73091869e-01],\n",
       "       [1.65462832e-02, 5.39773293e-01, 4.43680424e-01],\n",
       "       [1.91329828e-01, 6.10040799e-01, 1.98629373e-01],\n",
       "       [7.00239834e-02, 7.39973151e-01, 1.90002866e-01],\n",
       "       [2.18564468e-02, 4.07741397e-01, 5.70402156e-01],\n",
       "       [6.03705209e-02, 7.42675772e-01, 1.96953707e-01],\n",
       "       [7.09259448e-03, 5.66694326e-01, 4.26213079e-01],\n",
       "       [5.61276517e-02, 7.03646157e-01, 2.40226191e-01],\n",
       "       [1.25806135e-02, 3.02227995e-01, 6.85191392e-01],\n",
       "       [8.28959371e-02, 7.13059224e-01, 2.04044839e-01],\n",
       "       [4.80835304e-03, 5.13997866e-01, 4.81193781e-01],\n",
       "       [1.68297993e-02, 6.23157408e-01, 3.60012793e-01],\n",
       "       [5.71906766e-02, 7.34898207e-01, 2.07911117e-01],\n",
       "       [5.55267364e-02, 7.25493477e-01, 2.18979786e-01],\n",
       "       [1.58683195e-02, 6.93249718e-01, 2.90881963e-01],\n",
       "       [9.69248261e-03, 4.97881749e-01, 4.92425769e-01],\n",
       "       [2.33746897e-02, 5.13164902e-01, 4.63460408e-01],\n",
       "       [1.67613531e-01, 7.37830990e-01, 9.45554795e-02],\n",
       "       [5.62460456e-02, 6.99823056e-01, 2.43930898e-01],\n",
       "       [7.58478976e-02, 7.41516384e-01, 1.82635718e-01],\n",
       "       [8.18000161e-02, 7.02148748e-01, 2.16051236e-01],\n",
       "       [3.23384354e-03, 4.01614058e-01, 5.95152098e-01],\n",
       "       [1.91316397e-02, 3.64191732e-01, 6.16676628e-01],\n",
       "       [5.67068863e-02, 4.33342705e-01, 5.09950408e-01],\n",
       "       [3.15383177e-02, 6.52869981e-01, 3.15591701e-01],\n",
       "       [1.41297641e-02, 6.85199648e-01, 3.00670588e-01],\n",
       "       [7.56363185e-02, 5.67689701e-01, 3.56673981e-01],\n",
       "       [3.49588746e-02, 5.85624090e-01, 3.79417035e-01],\n",
       "       [1.72243598e-02, 5.42074645e-01, 4.40700996e-01],\n",
       "       [2.60400073e-02, 5.59425283e-01, 4.14534709e-01],\n",
       "       [5.41310585e-02, 6.94643245e-01, 2.51225696e-01],\n",
       "       [1.22453758e-01, 6.85551907e-01, 1.91994335e-01],\n",
       "       [3.29570376e-02, 5.65174546e-01, 4.01868417e-01],\n",
       "       [6.84734344e-02, 6.20159930e-01, 3.11366636e-01],\n",
       "       [5.14934365e-02, 5.83575331e-01, 3.64931232e-01],\n",
       "       [5.23458634e-02, 6.91219343e-01, 2.56434793e-01],\n",
       "       [2.81678732e-01, 6.05083090e-01, 1.13238178e-01],\n",
       "       [5.42860062e-02, 6.07143106e-01, 3.38570888e-01],\n",
       "       [6.12298896e-04, 1.88477423e-01, 8.10910278e-01],\n",
       "       [2.23323834e-03, 3.13212098e-01, 6.84554663e-01],\n",
       "       [8.94647365e-04, 3.49600583e-01, 6.49504770e-01],\n",
       "       [1.37232325e-03, 3.48906587e-01, 6.49721090e-01],\n",
       "       [8.04440162e-04, 2.81985542e-01, 7.17210018e-01],\n",
       "       [2.38110999e-04, 3.85496579e-01, 6.14265310e-01],\n",
       "       [4.96597713e-03, 2.98359887e-01, 6.96674136e-01],\n",
       "       [4.46330419e-04, 4.23445470e-01, 5.76108200e-01],\n",
       "       [5.55735066e-04, 4.20296367e-01, 5.79147898e-01],\n",
       "       [1.09716204e-03, 2.01057904e-01, 7.97844933e-01],\n",
       "       [6.83303973e-03, 3.14162859e-01, 6.79004102e-01],\n",
       "       [1.85701521e-03, 3.69366686e-01, 6.28776299e-01],\n",
       "       [1.93859131e-03, 3.28127126e-01, 6.69934283e-01],\n",
       "       [1.78467938e-03, 3.13790857e-01, 6.84424464e-01],\n",
       "       [1.71619772e-03, 2.14390008e-01, 7.83893794e-01],\n",
       "       [2.90845040e-03, 2.20527424e-01, 7.76564126e-01],\n",
       "       [2.23311020e-03, 3.63156271e-01, 6.34610619e-01],\n",
       "       [6.50753324e-04, 2.73636511e-01, 7.25712735e-01],\n",
       "       [6.27209874e-05, 4.12450339e-01, 5.87486940e-01],\n",
       "       [1.95681700e-03, 4.64763577e-01, 5.33279606e-01],\n",
       "       [1.48456291e-03, 2.65699846e-01, 7.32815591e-01],\n",
       "       [3.43381276e-03, 2.61949313e-01, 7.34616874e-01],\n",
       "       [1.60639695e-04, 4.23692247e-01, 5.76147114e-01],\n",
       "       [5.00899780e-03, 4.10177476e-01, 5.84813526e-01],\n",
       "       [1.92197509e-03, 2.73372601e-01, 7.24705424e-01],\n",
       "       [1.38204798e-03, 4.05000334e-01, 5.93617618e-01],\n",
       "       [7.25758596e-03, 3.95924949e-01, 5.96817465e-01],\n",
       "       [7.66944695e-03, 3.48624181e-01, 6.43706372e-01],\n",
       "       [9.44608520e-04, 3.16169430e-01, 6.82885962e-01],\n",
       "       [1.93724629e-03, 4.96664957e-01, 5.01397797e-01],\n",
       "       [5.65719326e-04, 4.30892515e-01, 5.68541765e-01],\n",
       "       [1.82255094e-03, 3.67663320e-01, 6.30514129e-01],\n",
       "       [8.70946529e-04, 2.99728135e-01, 6.99400918e-01],\n",
       "       [5.03552887e-03, 4.74013236e-01, 5.20951235e-01],\n",
       "       [1.11198418e-03, 4.34106398e-01, 5.64781618e-01],\n",
       "       [6.21226242e-04, 3.64419458e-01, 6.34959316e-01],\n",
       "       [1.78693912e-03, 1.73677991e-01, 8.24535070e-01],\n",
       "       [2.51867267e-03, 3.39283837e-01, 6.58197490e-01],\n",
       "       [9.27507092e-03, 3.41021439e-01, 6.49703491e-01],\n",
       "       [3.07835775e-03, 3.34847274e-01, 6.62074368e-01],\n",
       "       [1.32473963e-03, 2.43972861e-01, 7.54702400e-01],\n",
       "       [5.19663600e-03, 2.99202524e-01, 6.95600840e-01],\n",
       "       [2.23323834e-03, 3.13212098e-01, 6.84554663e-01],\n",
       "       [9.15227560e-04, 2.59435105e-01, 7.39649668e-01],\n",
       "       [1.33404228e-03, 2.00558247e-01, 7.98107711e-01],\n",
       "       [3.05605229e-03, 2.79345417e-01, 7.17598531e-01],\n",
       "       [2.52616378e-03, 3.92078093e-01, 6.05395744e-01],\n",
       "       [3.72888926e-03, 3.28941629e-01, 6.67329481e-01],\n",
       "       [2.90594978e-03, 1.78369605e-01, 8.18724445e-01],\n",
       "       [4.21535203e-03, 3.06014970e-01, 6.89769678e-01]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对每一行进行预测\n",
    "lr2.predict_proba(X)  #    0  1\n",
    "#.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d8bb75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d0c33b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c2904a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.切分数据集（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c589963b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size=0.3,random_state=420)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "61b61fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#查看C在L1、L2下训练集和测试集的表现\n",
    "l1 = []\n",
    "l2 = []\n",
    "\n",
    "l1test = []\n",
    "l2test = []\n",
    "\n",
    "for i in np.linspace(0.05,1,19):\n",
    "    #实例化模型并训练\n",
    "    lrl1 = LR(penalty='l1',solver='liblinear',C=i,max_iter=1000).fit(Xtrain,Ytrain)\n",
    "    lrl2 = LR(penalty='l2',solver='liblinear',C=i,max_iter=1000).fit(Xtrain,Ytrain)\n",
    "    \n",
    "    # 记录训练集的分数\n",
    "    l1.append(lrl1.score(Xtrain,Ytrain))\n",
    "    l2.append(lrl2.score(Xtrain,Ytrain))\n",
    "    \n",
    "    # 记录测试集的分数\n",
    "    l1test.append(lrl1.score(Xtest,Ytest))\n",
    "    l2test.append(lrl2.score(Xtest,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ef18d4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "\n",
    "graph = [l1,l2,l1test,l2test]\n",
    "color = [\"green\",\"gold\",\"lightgreen\",\"orange\"]\n",
    "label = [\"L1\",\"L2\",\"L1test\",\"L2test\"] \n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "for i in range(len(graph)):\n",
    "    plt.plot(np.linspace(0.05,1,19),graph[i],color[i],label=label[i])\n",
    "plt.legend(loc=4) #图例的位置在哪⾥?4表示，右下⻆\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e4c767",
   "metadata": {},
   "outputs": [],
   "source": [
    "可见，至少在我们的鸢尾花数据集下，两种正则化的结果区别不大。但随着C的逐渐变大，正则化的强\n",
    "度越来越小，模型在训练集和测试集上的表现都呈上升趋势，直到C=0.9左右，训练集上的表现依然在走\n",
    "高，但模型在未知数据集上的表现平缓，这时候就是出现了     。我们可以认为，C设定为0.9会\n",
    "比较好。在实际使用时，基本就默认使用L2正则化，如果感觉到模型的效果不好，那就换L1试试看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f56d0752",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f92fc83648>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 鸢尾花数据集下，max_iter的学习曲线∶\n",
    "# 确定C=0.9  关于最大迭代次数绘制学习曲线\n",
    "l2 = []\n",
    "l2test = []\n",
    "\n",
    "for i in range(1,201,10):\n",
    "    lrl2 = LR(penalty='l2',solver='liblinear',C=0.9,max_iter=i).fit(Xtrain,Ytrain)\n",
    "    \n",
    "    l2.append(lrl2.score(Xtrain,Ytrain))\n",
    "    l2test.append(lrl2.score(Xtest,Ytest))\n",
    "    \n",
    "    \n",
    "plt.plot(range(1,201,10),l2,label='l2')\n",
    "plt.plot(range(1,201,10),l2test,label='l2test')\n",
    "plt.legend(loc=4)\n",
    "\n",
    "# 当max_iter中限制的步数已经走完了，逻辑回归却还没有找到损失函数的最小值，参数w的值还没有被\n",
    "收敛，sklearn就会弹出下面的红色警告\n",
    "当参数solver=\"liblinear\"∶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f2017f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "这是在提醒我们：参数没有收敛，请增大max_iter中\n",
    "输入的数字。但我们不一定要听sklearn的。max_iter很大，意味着步长小，模型运行得会更加缓慢。然\n",
    "我们在梯度下降中追求的是损失函数的最小值，但这也可能意味着我们的模型会过拟合（在训练集上表\n",
    "现得太好，在测试集上却不一定），因此，如果在max iter报红条的情况下，模型的训练和预测效果都\n",
    "已经不错了，那我们就不需要再增大max_iter中的数目了，毕竟一切都以模型的预测效果为基准&#x2029;",
    "一只要\n",
    "最终的预测效果好，运行又快，那就一切都好，无所谓是否报红色警告了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "31e6d31f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7], dtype=int32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们可以使⽤属性.n_iter_来调⽤本次求解中真正实现的迭代次数\n",
    "lr = LR(penalty=\"l2\",solver=\"liblinear\",C=0.9,max_iter=300).fit(Xtrain,Ytrain)\n",
    "lr.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "396fb045",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b57c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.使用标准化包，对训练集来学习，从而对训练集和测试集来做标准化（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dd0bd0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导包\n",
    "from sklearn.model_selection import GridSearchCV #网格搜索\n",
    "from sklearn.preprocessing import StandardScaler #标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a83b91e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(X,columns= load_iris().feature_names)\n",
    "data['label'] = Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "47429055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   label  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f482a70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#划分数据集\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size=0.3,random_state=420)\n",
    "\n",
    "#对训练集和测试集做标准化---去量纲\n",
    "std = StandardScaler().fit(Xtrain)\n",
    "Xtrain_ = std.transform(Xtrain)\n",
    "Xtest_ = std.transform(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a37d8949",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e613483d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.在确定l2范式的情况下，使用网格搜索判断solver, C的最优组合（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "caf67a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=10000),\n",
       "             param_grid={'C': [0.05, 0.10277777777777777, 0.15555555555555556,\n",
       "                               0.20833333333333331, 0.2611111111111111,\n",
       "                               0.3138888888888889, 0.36666666666666664,\n",
       "                               0.41944444444444445, 0.4722222222222222, 0.525,\n",
       "                               0.5777777777777778, 0.6305555555555556,\n",
       "                               0.6833333333333333, 0.7361111111111112,\n",
       "                               0.788888888888889, 0.8416666666666667,\n",
       "                               0.8944444444444445, 0.9472222222222223, 1.0],\n",
       "                         'solver': ['liblinear', 'sag', 'newton-cg', 'lbfgs']})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在l2范式下，判断C和solver的最优值\n",
    "p = {\n",
    "    'C':list(np.linspace(0.05,1,19)),\n",
    "    'solver':['liblinear','sag','newton-cg','lbfgs']\n",
    "}\n",
    "\n",
    "model = LR(penalty='l2',max_iter=10000)\n",
    "\n",
    "GS = GridSearchCV(model,p,cv=5)\n",
    "GS.fit(Xtrain_,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff339351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714285714285715"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2ddc8a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.41944444444444445, 'solver': 'sag'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dae28095",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5.将最优的结果重新用来实例化模型，查看训练集和测试集下的分数（20分）\n",
    "(注意多分类需要增加参数  average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "07cfbb05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将最优参数重新用于实例化模型，查看训练集和测试集下的分数\n",
    "model = LR(penalty='l2',\n",
    "           max_iter=10000,\n",
    "           C=GS.best_params_['C'],\n",
    "           solver=GS.best_params_['solver'])  #sag 三种通过导数计算的方式是不能l1正则化的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8933492a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.41944444444444445, max_iter=10000, solver='sag')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain_,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4ee22969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9714285714285714, 0.9555555555555556)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(Xtrain_,Ytrain),model.score(Xtest_,Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20f2826",
   "metadata": {},
   "outputs": [],
   "source": [
    "#6.计算精准率（20分）    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f7bc51c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR 模型训练集准确率：0.971\n",
      "LR 模型测试集准确率：0.956\n"
     ]
    }
   ],
   "source": [
    "print('LR 模型训练集准确率：%.3f'%model.score(Xtrain_,Ytrain))\n",
    "print('LR 模型测试集准确率：%.3f'%model.score(Xtest_,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf5d4678",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "67e93195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR 模型训练集准确率：0.9714285714285714\n",
      "LR 模型测试集准确率：0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "print('LR 模型训练集准确率：%s'%model.score(Xtrain_,Ytrain))\n",
    "print('LR 模型测试集准确率：%s'%model.score(Xtest_,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83c3ff18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%的⽤法，⽤%来代替打印的字符串中，想由变量替换的部分。%.3f表示，保留三位⼩数的浮点数。%s表示，字符串。\n",
    "#字符串后的%后使⽤元祖来容纳变量，字符串中有⼏个%，元组中就需要有⼏个变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144827a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce0760c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09014ca0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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